Recommendation techniques are increasingly being used to provide relevant and enjoyable information to users based on users' feedback and stated preferences. One of the most common and effective recommendation techniques is Collaborative Filtering (CF), which relies only on past user behavior (e.g., previous transactions or feedback), and does not require creations of explicit profiles.
FIG. 1A, FIG. 1B and FIG. 1C (Prior Art) illustrate a user rating prediction process based on CF, according to a prior art system. As shown in FIG. 1A, users 101-105 provides rating regarding different items (like images 111, books 112, videos 113, games 114). After that, the system is making predictions about user 105's rating for the item 113, which the user 105 has not rated yet. The prediction can be made based on existing ratings of other users, who have similar ratings with the user 105. As shown in FIG. 1B, users 102 and 103 have similar ratings with the user 105 regarding other items. Therefore, user 105's rating for the item 113 is estimated based on users 102 and 103's ratings for the item 113. As shown in FIG. 1C, the system has made a prediction that the user 105 won't like the video 113, as users 102 and 103.
A problem arising when employing CF techniques is the cold-start problem, which is caused by the system's incapability of dealing with new items or new users due to the lack of relevant transaction history. To mitigate the item-cold problem of CF, existing techniques focus on utilizing external content on top of users' feedback. When a new item comes, the existing techniques leverage the new item's attributes and combine them with the CF model. Thus, existing works require the new item's content or context data that may not be available. In addition, traditional systems for estimating user interests with respect to a new item did not provide a way to effectively select a subset of users for obtaining interests with respect to the new item and estimate interests of all users based on the obtained interests.
Therefore, there is a need to provide a solution for estimating user interests with respect to a new item to avoid the above-mentioned drawbacks.